Molecular Mechanism(s) of Sex Differences in Lipid Metabolism in Human Skeletal Muscle
Bibliographic record
Abstract
It is well understood that compared with men, women are better able to withstand starvation, have better ultra-endurance capacity, oxidize more fat during endurance exercise, and are more resistant to fat oxidation defects i.e. diet-induced insulin resistance. However, the mechanism(s) for the observed sex differences are unknown. It was my hypothesis that women have greater fat oxidation capacity in skeletal muscle than men. The objectives of my thesis were to determine the mechanism(s) by which women oxidize more lipids; including the role of estrogen as a possible regulator. The most significant findings were that: 1) mRNA for fatty acid oxidation genes are higher in women compared with men, which was confirmed by Stringent Affymetrix GeneChip array analysis, combined with RT-PCR (chapter 2); 2) long-chain acyl-CoA dehydrogenase in human skeletal muscle is not quantifiable despite the majority (90%) of fatty acids oxidized during exercise are long-chain fatty acids (chapter 3); 3) β-oxidation enzymes: tri-functional protein alpha, very long chain acyl-CoA dehydrogenase, and medium chain acyl-CoA dehydrogenase are significantly higher in women compared with men (chapter 4); 4) Acute (8 days) 17β-estradiol supplementation in men significantly increased protein content of β-oxidation enzymes in skeletal muscle, possibly through the regulation of PGC-1α and microRNA (chapter 5). In conclusion, my data provided novel insights into the enhanced ability of women to oxidize fat under periods of metabolic stress by showing that: 1) women are transcriptionally (mRNA) "primed" for known physiological differences in metabolism; 2) women have more protein content of the major enzymes involved in long and medium chain fatty acid oxidation; 3) E2 partially regulates lipid metabolism in skeletal muscle by pre-translational modifications of factors involved in β-oxidation. These findings contribute to the molecular understanding of sex differences in substrate utilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".